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The effects of various factors, namely the shape of the probe, the contact between the probe and sample and the modelling method, were also evaluated.
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Key features of the modelling method are described, as are the stages of modelling and the associated use of proprietary modelling tools.
The modelling method is characterised by a decomposition of the model into interacting submodels and by a stepwise introduction of simplifying assumptions on different decomposition levels.
In this paper, the parameter selection capabilities of the group method of data handling (GMDH) as an inductive self-organizing modelling method are used to construct sparse random sampling high dimensional model representations (RS-HDMR), from which the Sobol's first and second order global sensitivity indices can be derived.
The numerical results of the fuzzy modelling method are compared with the results of a conventional linear regression model.
An application of this modelling method is realized by commercial software packages by importing fully coupled ANSYS© – models in MATLAB©.
An efficient dynamic modelling method is presented lumped stiffness method.
The modelling method is validated by comparison with experiments.
The modelling method is validated on test cases.
Benefits and limitations of each modelling method are discussed.
So, still a modelling method is needed which allows exact prediction of strength and stiffness.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com